applied AI-driven, human-in-the-loop extraction from unstructured clinical notes to recover smoking-status data, improving data completeness and trial-feasibility/AI performance
applied AI-driven, human-in-the-loop extraction from unstructured clinical notes to recover smoking-status data, improving data completeness and trial-feasibility/AI performance
The customer is anonymised in TriNetX’s own case study — not withheld by us.
TriNetX is a federated real-world-data network where data stays behind each health system's firewall and is queried in place, used for cohort discovery and trial-site feasibility.
This sits in the Data Providers stage of the AI use-case lifecycle: Named examples from the real-world-data, claims and competitive-intelligence providers pharma AI systems are trained on, benchmarked against, or run inside of.
Published by TriNetX on its own site — read the original case study on trinetx.com ↗
Note: Original URL 301-redirects to https://www2.trinetx.com/clinical-data-enrichment-recovering-smoking-status-to-improve-trial-feasibility-and-ai-performance, which renders as a JS-driven page with no static/extractable text (confirmed on two separate fetch attempts) -- effectively an empty JS shell.
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